Human action recognition in videos based on the Transferable Belief Model: Application to athletics jumps

Authors:
E. Ramasso;C. Panagiotakis;D. Pellerin;M. Rombaut
Affiliations:
GIPSA-Lab, DIS Department, 46 avenue Félix Viallet, 38031, Grenoble, France;University of Crete, Department of Computer Science, P.O. Box 2208, 46 avenue Félix Viallet, 38031, Heraklion, Greece;GIPSA-Lab, DIS Department, 46 avenue Félix Viallet, 38031, Grenoble, France;GIPSA-Lab, DIS Department, 46 avenue Félix Viallet, 38031, Grenoble, France
Venue:
Pattern Analysis & Applications
Year:
2007

Citing 0
Cited 3

Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions

ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Particle Filtering and DSmT Based Approach for Conflict Resolving in case of Target Tracking with Multiple Cues

Journal of Mathematical Imaging and Vision
Vision-based motion detection, analysis and recognition of epileptic seizures-A systematic review

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transferable Belief Model framework is proposed and applied to action recognition of an athlete in video sequences of athletics meeting with moving camera. Relevant features are extracted from videos, based on both the camera motion analysis and the tracking of particular points on the athlete’s silhouette. Some models of interpretation are used to link the numerical features to the symbols to be recognized, which are running, jumping and falling actions. A Temporal Belief Filter is then used to improve the robustness of action recognition. The proposed approach demonstrates good performance when tested on real videos of athletics sports videos (high jumps, pole vaults, triple jumps and long jumps) acquired by a moving camera and different view angles. The proposed system is also compared to Bayesian Networks.